Carolina Guide: A Multi-Agent RAG System with Institutional Guardrails for Academic Policy Assistance
arXiv:2606.28360v1 Announce Type: cross Abstract: University students often struggle to navigate complex academic policies, leading to advising bottlenecks and delayed access to critical information. Although large language models (LLMs) offer promise for automated assistance, their tendency toward hallucination and inability to enforce institutional constraints make them unsuitable for high-stakes policy guidance without careful architectural design. We present Carolina Guide, a retrieval-augmented generation (RAG) system for academic policy assistance at the University of South Carolina (USC
The proliferation of powerful LLMs and the urgent need for reliable, constrained AI applications in institutional settings are driving the development of specialized RAG systems.
This demonstrates a crucial step towards safely deploying AI in high-stakes environments, addressing key limitations of current LLMs by integrating institutional guardrails and retrieval augmentation.
The ability to deploy AI agents for policy guidance with reduced hallucination and enforced compliance opens new avenues for operational efficiency and knowledge dissemination within complex organizations.
- · Universities and large institutions
- · AI safety and ethics researchers
- · RAG system developers
- · Students and employees navigating complex policies
- · Generic, unconstrained LLM vendors
- · Traditional human-only advising services
- · Organizations slow to adopt secure AI solutions
Universities will begin to reduce advising bottlenecks and improve access to accurate policy information.
The success of institutional RAG systems will accelerate their adoption across other regulated sectors like healthcare and finance.
The development of highly reliable, institution-specific AI agents could lead to a re-evaluation of human roles focused on empathy and complex problem-solving rather than rote information dissemination.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI